lmshuizong

function [Coefficients,residual,pitch,Gain,parcorCoefficients,stream,B,crossmat,error_stream] = lpcanalysis(speechtodecode,samplerate,L,windowtime,preemp)

通常语音信号在增强时会出现混响现象，演讲者为了消除背景混响，不得不频繁地偏转头部的方向，这样会造成脉冲响应的不断改变。我们结合盲解卷法和频谱消去法来提高逆滤波器的滤波效果。我们利用输入语音信号间的相关系数矩阵计算出稳定、精确的室内脉冲响应的逆滤波器，而这些输入信号无需测量室内的脉冲响应就能被观测到。逆滤波能够消除早期的反射，这些反射包含混响中的绝大部分能量。之后，用频谱消去法来抑制逆滤波后的信号的尾部混响。本方法在实际适应性方面的表现通过具体的实验进行了验证，结果表明盲解卷法和频谱消去法的结合相较于单独使用一种方法，能够提供一个更优越的演说环境。(Usually voice signal will be enhanced when the reverberation phenomenon, the speaker in order to eliminate the background reverberation, and had to frequently deflect the direction of the head, this will cause the impulse response of changing. We combine the blind deconvolution and spectral elimination method to improve the filtering effect of the inverse filter. We use the correlation coefficient matrix between the input speech signal to calculate the stable, accurate inverse filter of the indoor impulse response, and these input signals do not need to measure the room impulse response can be observed. Inverse filtering to eliminate early reflections, these reflections contains most of the energy in the reverberation. After the spectrum elimination method to suppress the tail reverberation of the signal after the inverse filtering. Performance in the actual adaptation of the method by specific experimental validation results show that the blind deconvolution and spectrum to eliminate )

运用HMM进行语音识别之后的孤立词识别，里面函数俱全。可以直接运用，简单方便(Speech recognition with HMM isolated word recognition, which function and taste. Can directly use, simple and convenient)

从wave中读取数据，通过mfcc提取特征系数。从txt文件中读取基于GMM的分类器，最终识别为那种类型(Read data from the wave through the MFCC feature extraction coefficient. Txt file to read from the GMM-based classifier, and ultimately identified as the type)

一个HMM的Matlab实现方法，可实现孤立词语音识别(Matlab implementation of a HMM methods, an isolated word speech recognition can be achieved)

均匀线列阵和均匀平面阵的自然指向性图的画法(just a little program)

用自相关函数提取语音信号基频，提取音频文件的基频等高线(Use the autocorrelation function on segments of the signal (windowsize: 100ms) and compute the fundamental frequency. Use a max_time_lag of 100ms in the autocorrelation function and a window shift of 25ms. Create a fundamental frequency vector and plot the pitch contour.)

用NLMS实现对语音的回声的消除,共4个文件,语音原声,语音回声,NLMS的实现,回声路径(Using NLMS realize voice echo on the elimination of a total of four documents, voice, soundtrack, voice echo, NLMS realization echo path)

绘制语音信号的 WVD 分布，对时频工具箱中的 tfrwv 及 tfrspwv 函数的使用进行了详细说明(The way of drawing speech signals WVD was shown in the m file, and corresponding functions tfrwv and tfrspwv were discussed thoroughly.)

characteristic parameters for speech recognition on the extraction function